Introduction to Static Reservoir Modeling

PEMODELAN STATIS

INTRODUCTION TO STATIC RESERVOIR MODELING

TRAINING SCHEDULE Time Event 09.00-10.30 Introduction 10.30-10.45 Break 10.45-12.00 Geological Control 12.00-13.00 Break 24-Mei-2014 13.00-14.00 Well Correlation 14.00-14.15 Break 14.15-16.00 Seismic Interpretation 16.00-16.15 Homework

25-Mei-2014

09.00-09.30 09.30-10.30 10.30-10.45 10.45-12.00 12.00-13.00 13.00-15.00 15.15-16.00

Review Geostatistic Break Geometry Modeling Break Facies & Property Modeling Volumetric & Uncertainty

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OIL & GAS UPSTREAM BUSSINES PROCESS

OIL & GAS UPSTREAM BUSSINES PROCESS

PREPARATION

Acquiring Contract Area

EXPLORATION

DEVELOPMENT

Resources  Reserves

Reserves  Production

PRODUCTION

Product Optimization

MARKETING

Finding Market

SKKMIGAS, 2013

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GEOLOGICAL MODELING

HISTORICAL PERSPECTIVE Suppose you are required to prospect a very large area for gold. You have all the necessary tools for drilling to mine a spot for gold. However, due to costs and technical difficulty you do not have the luxury to mine physically the whole area (with extensive drilling) in order to find out the locations where gold is deposited in high amounts. Another problem that complicates your objective is that there is no precedence of gold mining in your area (i.e., no body really knows the geology or any historical fact to guide you to choosing drilling locations that may have a high probability of having gold deposits.) So what do you do? (the founder of geostatistics Dr. Krige in South Africa was faced with the same problem some 80 years ago)

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GEOSTATISTICS “Geostatistics defined as the branch of statistical sciences that studied spatial/temporal phenomena and capitalizes on spatial relationship to model possible value(s) at unobserved, unsample location.” (Caers, 2005) Geostatistics concept:  Quantify Spatial Relationship (i.e. by using Variogram) The non-randomness of geological phenomena entails that value measured close to each other are more “alike” than value measure farther apart.  Modeling Spatial Relationship  Estimation: Kriging  Simulation: Conditional Simulation (SGS/SIS/TGS)

GEOLOGICAL MODELING Geomodeling consists of the set of all the mathematical methods allowing to model in an unified way the topology, the geometry and the physical properties of geological objects while taking into account any type of data related to these objects. (Mallet, 2002) A Geomodel is the numerical equivalent of a three-dimensional geological map complemented by a description of physical quantities in the domain of interest. (Mallet, 2008) Geologic modeling or Geomodeling is the applied science of creating computerized representations of portions of the Earth's crust based on geophysical and geological observations made on and below the Earth surface. (Wikipedia)

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WHY DO WE NEED GEOMODEL? 3D models help us visualize the ground beneath our feet without the need for training in complex geological techniques. Modelling the Earth's subsurface can help us understand the relationship between geology and our environment. Our traditional printed, 2D geological maps show the distribution of geological units at the surface, but 3D models of the same geology shows us the depth of features such as faults, changes in thickness, tilted units and subsurface contacts. 3D models can:  allow non IT specialists to easily access geological information  answer specific questions about the subsurface  produce a range of outputs  display 360° views

DEVELOPMENT OF GEOMODEL In the 70's, geomodelling mainly consisted of automatic 2D cartographic techniques such as contouring, implemented as FORTRAN routines communicating directly with plotting hardware. The advent of workstations with 3D graphics capabilities during the 80's gave birth to a new generation of geomodelling software with graphical user interface which became mature during the 90's Since its inception, geomodelling has been mainly motivated and supported by oil and gas industry.

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APPLICATION Geomodeling Application

Mining

Petroleum

Basin

Geothermal

Reservoir

Unconvention al

Conventional

Silisiclastics

Carbonate

Hydrology

Basement

Tight Sand

Shale Hydrocarbon

Coal Bed Methane

BASIN & RESERVOIR MODELING

Basin Modeling Looks into larger aspects like existence of a petroleum system in the area Aim is to predict  Reservoir development, Source rock maturation,  Migration history, Thermal history,  Pressure development etc.

Reservoir Modeling Looks into finer aspects of the reservoir  Static  Static model  Presents the current geologic setup  Presents the current state of tectonic deformation  Presents the current state of stratigraphy  Models current distribution of rock properties

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CONVENTIONAL & UNCONVENTIONAL

Conventional  Hydrodynamic emplacement and trapping  Controlled by local structure and stratigraphy • Well defined limits (e.g. seal and fluid contact) • Discrete fields  Un-stimulated Production

Unconventional  Trapping not hydrodynamic  Controlled by regional stratigraphy  Poorly defined limits  “Continuous” or “Dispersed” Accumulations  Requires stimulation / de-watering

SOURCE OF DATA Source of data are reservoir modeling:  Geological Data – any data related to the style of geological deposition:  Core data – porosity, permeability, and relative permeability per facies  Well log data – any suite of logs that indicate lithology, petrophysics, and fluid types near the wellbore  Sedimentological and stratigraphic interpretation  Outcrop analog data  Geophysical Data – any data originating from seismic surveys:  Surface and fault interpreted on 3D seismic  Seismic Attribute  Rock physics data  Reservoir Engineering Data – any data related to the testing and production of the reservoir:  Pressure/volume/temperature (PVT) data.  Well-test data  Production data

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ROLE OF GEOMODELER  Data QC and data harmonization (structural, sedimentological, petrophysical, geophysical and geomechanical analysis)  Elaboration of conceptual model as an integrated process that involves experts from various fields  Structural modeling: Incorporate relevant structural elements and delineate different fault blocks  Gridding of target area  Facies Modeling (Sequential Indicator Simulation (SIS), Truncated Gaussian Simulation (TGS), object based modeling or Multi Point Statistics (MPS))  Petrophysical Modeling: Geostatistical data analysis and simulation (Sequential Gaussian Simulation (SGS) and co-simulation)  Water saturation modeling (J-function analysis)  Static Model upscaling  Uncertainty Analysis: Visualize dependencies between the input parameters (seismic, structure, facies, petrophysics) and quantification and visualization of the spatial location and variability of the uncertainty  Discrete Fractured Network modeling (DFN)

GEOLOGICAL CONTROL

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SILISICLASTICS

CARBONATES

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FRACTURED BASEMENT

SHALE HYDROCARBON

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COAL BED METHANE

End of Slide Show

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End of Slide Show

WELL CORRELATION

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Scope of discussion  Sequence

Stratigraphy Concepts  Electrofacies  Regional Geology of Jambi Sub-Basin  Core Description  Sequence Stratigraphy Correlation

Sequence Stratigraphy Concepts

Sediment patterns in siliciclastic non-marine and shelf deposits are controlled by two fundamental parameters : 1. The rate of sediment influx (Sedimentation rate) 2. Changes in the potental space available for sedimentation (Space accomodation)

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Sequence Stratigraphy Concepts

Sequence Stratigraphy Concepts

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Sequence Stratigraphy Concepts

Boyd & Diesel, 1994

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Electrofacies

Serra. O, 1985

Electrofacies

Fluvial Environment

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Electrofacies

Incised Valley and Estuarine Environment

Electrofacies

Delta Environment

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Electrofacies

Deepwater Submarine and Turbidite Environment

Electrofacies

Deepwater Submarine and Turbidite Environment

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Core Description Sequence Statigraphic Analysis of Well Log Previous Study Interval: 1219.00 - 1229.43 M

top

bottom

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FERG-2

Lowstand aggradation

Core interval

Highstand progradatio nal Transgresisi ve retrogradati onal

B

Lowstand aggradation

Lower Pendopo

Transgresisi ve retrogradati onal

Upper Pendopo

Interval: 1219.00 - 1229.43 m / 3999.344 - 4033.563 ft

A

End of Slide Show

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Sedimentology and Stratigraphy Review for Static Modeling

Scope of discussion 

Important of sedimentology and stratigraphy in static modeling



Definition review



Aim of sedimentology and stratigraphy in static modeling



Scale of observation



Reservoir Geometry

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PEMODELAN STATIS

Important of sedimentology and stratigraphy in static modeling (Examples)

Almost on Sedimentary Rocks



Introduction



Geology control



Silisiclastic



Correlation and Seismic Picking



Geostatistic



Geometrical modelling



Property Modelling



Volumetric

Sedimentology and Stratigraphy Factor

Outline of our discussion :

Geological understanding need

Geological Factor

Definition review

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Definition review Sedimentology of the scientific study of sediments (unconsolidated) and sedimentary rocks (consolidated) in terms of their description, classification, origin and diagenesis (Shanmugam, 2006).

Reading (1986) suggested four steps for reconstructing ancient environments: (1) description of the rocks; (2) interpretation of processes; (3) establishment of vertical and lateral facies relationships; and (4) use of modern analogs.

Good News!!

Sedimentology field activities

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Definition Review Stratigraphy is a branch of geology which studies rock layers (strata) and layering (stratification)(Wikipedia.org). Some stratigraphic subfields : 

Lithologic stratigraphy



Biologic stratigraphy



Chronostratigraphic



Magnetostratigraphic



Archeological stratigraphy

Definition Review Sequence stratigraphy is a methodology that provides a framework for the elements of any depositional setting, facilitating paleogeographic reconstruction and the prediction of facies and lithologies away from control point (Catuneanu, 2011)

This framework ties changes in stratal stacking patterns to the responses to varying accomodation and sediment suplly through time.

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Aim of sedimentology and stratigraphy in static modeling Data should be talking about geological processes and feature, not only statistic and useful for hydrocarbon exploration and production. What geological processes and feature means : 

Geometry of sand body would be filled by hydrocarbon.



Depositional environment and paleogeography.

Scale of observation • Stage I : Geological Assesment • provides a description of the sandbody dimensions, geometry, and connectivity. • Stage II : Petrophysical Evaluation • focuses on the rock and fluid systems at a much smaller scale, i.e. the pore scale. • Stage III : Formation Evaluation • pore-scale descriptions from Stage II are upscaled and integrated into continuous profiles of porosity, permeability, water saturation, and hydraulic rock types at the wellbore • Stage IV : Reservoir Modeling : Sedimentology and stratigraphy applied : Sedimentology and stratigraphy model applied

Gunter et al (1997)

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Scale of observation

• Mini-scale • Core description include lithology, sedimentary structure and textural atribute.

Scale of observation

• Meso-scale • Upscaled interpretation of the vertical distribution of the depositional rock type and identification of the processes influencing their vertical distribution.

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Scale of observation

•

Mega-scale • The associated geologic processes and the depositional rock types are interpreted in terms of depositional environments that further provide insights into the initial reservoir dimensions, geometry, position, and connectivity.

Reservoir Geometry Mini-Scale

Meso-Scale

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Reservoir Geometry Mega Scale

End of Slide Show

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GEOSTATISTICS IN RESERVOIR MODELING

OUTLINE Introduction Some

basic definition Spatial Statistics Deterministic Modeling Stochastic Modeling

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INTRODUCTION

What is Geostatistics? 

“Geostatistics: study of phenomena that vary in space and/or time” (Deutsch, 2002)



“Geostatistics can be regarded as a collection of numerical techniques that deal with the characterization of spatial attributes, employing primarily random models in a manner similar to the way in which time series analysis characterizes temporal data. (Olea, 1999)



“Geostatistics offers a way of describing the spatial continuity of natural phenomena and provides adaptations of classical regression techniques to take advantage of this continuity.” (Isaaks and Srivastava, 1989)



Statistical technique that accounts for spatial relationships of variables in estimating values of the variables at unsampled locations. (Kelkar and Perez, 19??)

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Application of Geostatistics



Interpolation and Extrapolation



Spatial Distribution Analysis



Risk Analysis/Uncertainty Estimates



Use of Intercorrelated Attributes

Limitations of Geostatistics • • • •

Geostatistics Does Not “Create” Data or Eliminate the Value of Obtaining Additional Good Data Geostatistics Does Not Replace Sound Qualitative Understanding and Expert Judgment Geostatistics Does Not Necessarily Save Time, At Least in the Short Term. Geostatistics Does Not Work Well as a “Black Box” Porosity at X is 13.7%

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Reservoir Modeling 

Some basic definition

BASIC DEFINITION

STATIC RESERVOIR MODEL Parameters which does not change in time ie: Facies, Reservoir Rock Type (RRT), Phi, Initial Sw, etc.

DYNAMIC RESERVOIR MODEL Parameters that change in time ie: Fluid flow, Pressure, etc.

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HOMOGENY Vs. ISOTROPY Homogeny & Heterogenic

Vs.

Isotropy & Anisotropy

a)

b)

c)

d)

Anisotopy: a) 1 b) 0.8 c) 0.5 d) 0.2

The direction of Maximum continuity

high Heterogeneity

Low Heterogeneity

The direction of Minimum continuity

STATIONARY

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Mean Value Arithmetic

Geometric

Harmonic

Deterministic Vs Stochastic Deterministic If One Knows Enough About the Process Responsible for the Distribution Stochastic If the Underlying Process Is Not Well Understood •

•

Deterministic Models Depend on Outside Information Not Contained in the Data Values (i.e. Quantitative Process Description) and the Context of the Data Deterministic Model Examples: • Distance a Ball Will Travel When Thrown • Information Needed • • •

Equation Velocity and Angle Ball Is Thrown Gravitational Constant (g)

•

Stochastic Models

• Stochastic Models Are Useful When the Process Responsible for the Distribution of Values is Not Well Understood • A Stochastic Model is a “Random Model” Controlled by a Spatial Correlation Model • Stochastic Models are a Useful Reservoir Characterization Tool Because a Reservoir is the End Product of Many Poorly Understood Processes

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PEMODELAN STATIS

Estimation Vs Simulation Estimation is Process of Obtaining the Single Best Value of a Reservoir Property at an Unsampled Location. Local Accuracy Takes Precedence Over Global Spatial Variability. Estimation Methods, Therefore, Tend to Produce “Smooth” Property Distributions. Many Traditional Methods Block Averages Inverse Distance Weighted Interpolation Triangulation

Simulation is Process of Obtaining One or More Good Values of a Reservoir Property at an Unsampled Location. The Simulated Distributions Honor Global Features and Statistics Instead of Local Accuracy. Simulation Methods Tend to Produce More Realistic Property Distributions. Variety of Methods Available, Including: Gaussian Sequential Simulation (GSS) Sequential Indicator Simulation (SIS) Simulated Annealing Boolean (Marked-Point, Object Based)

Many Geostatistical Methods Ordinary Kriging Collocated Cokriging

Estimation Vs Simulation Estimation

Simulation

Note Smooth Contours On Estimation Map Compared to Simulation (Stochastic) Map. Note that Areas of Greatest Difference Between the Two Maps Are In Areas of Little or No Well Control.

Effective Porosity

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SPATIAL STATISTICS

Spatial Analysis • Characteristics of Geoscience Data Sets : Exhibit Spatial Relationships • neighboring values are related to each other • The relationship gets stronger as the distance between two neighbors becomes smaller • In most instances, beyond certain distance the neighboring values becomes uncorrelated • Statistical methods to quantify spatial relationship: • Covariance • Variogram

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Covariance

Variogram

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Covariance Vs. Variogram • Covariance measures similarities whereas variogram measures the difference • Relationship under most situations • In geostatistics, we use variogram instead of covariance to describe spatial relationship

Covariance

Variogram

Variogram

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DETERMINISTIC MODELING 

Estimation Process - Kriging

ESTIMATION • Estimation means the process to estimate the value at interwell locations. • Common method : Linear Interpolation. • Linear Interpolation in Geostatistics is done using Kriging • Kriging is named after it founder Danny Krige, a gold miner scientist from South Africa (1948) • Kriging is a deterministic method. • The main difference between kriging and conventional linear interpolation is the use of spatial relationship (i.e., variogram), instead of based on pre-defined formula.

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LOCAL ESTIMATION • Point Estimation Methods

– Geological Experience and/or Artistic License – Traditional Algorithms That Use Weights Based on Euclidean (Geometric) Distance • • • •

Polygon Method (Nearest Neighbor) Triangulation Local Sample Mean Inverse Distance

– Geostatistical Algorithms That Use Weights Based on “Structural” (or Statistical) Distance • • • • •

Simple Kriging Ordinary Kriging Universal Kriging Kriging with Trend Collocated Cokriging

ESTIMATION PROCESS - KRIGING

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Stochastic modeling

SEQUENTIAL SIMULATION • The most popular technique in reservoir description • Uses grid based method • Can generate multiple realizations of various reservoir attributes • The two common most methods are: Sequential Indicator Simulation (SIS) and Sequential Gaussian Simulation (SGS) • TGS : • Combination of SGS and SIS • Provide smoother distributin of discrete variable • To honor local relationships among various attributes, cosimulation method is used

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SEQUENTIAL SIMULATION PROCEDURE: • Transform • Variogram Analysis • Random Path Determination • Kriging • Uncertainty Quantification • Back Transform

Transform Gaussian Transform: • Transform the data (may be originally as continuous or discrete variable) to become Continuous variable • In most cases, SGS is used for continuous variable but, it may also be used for discrete variable (e.g., TGS)

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Sequential Gaussian Simulation based on Simple Kriging

4 realizations

Sequential Gaussian Simulation based on Simple Cokriging

4 realizations

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Example – Sequential Gaussian Cosimulation (1)

4 realizations

Example – Sequential Gaussian Cosimulation (2)

4 realizations

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End of Slide Show

STATIC MODELING

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STATIC MODELING 

PENDAHULUAN



WORKFLOW



DATA YANG DIBUTUHKAN



MODEL GRID



MODEL FACIES



MODEL PETROFISIKA



PERHITUNGAN VOLUMETRIK



ANALISIS SENSITIVITAS DAN KETIDAKPASTIAN



UPSCALE

PENDAHULUAN

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DEFINISI UMUM

STATIC RESERVOIR MODEL Parameters which do not change in time ie: Facies, Reservoir Rock Type (RRT), Phi, etc. Permeability ? Water Saturation ?

DYNAMIC RESERVOIR MODEL Parameters that change in time ie: Fluid flow, Pressure, etc.

WORKFLOW

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WORKFLOW Petrophysical Intepretation

Geological Intepretation

Geophysical Intepretation

Static Model (base case)

Well Test DST/MDT/RFT

Bubble Map

Dynamic Data Validation

Material Balance

Uncertainty Analysis

Scale Up

Overall Workflow

WORKFLOW Input Data

Model Grid

Model Facies

Model Petrofisika

Perhitungan Volumetrik

UPSCALING

Intepretasi Petrofisika

Model Patahan

Scale Up Well Log

Scale Up Well Log

OOIP/OGIP

Design

Intepretasi Geofisika

Areal Gridding

Analisis Geostatistik

Analisis Geostatistik

Analisis Sensitivitas

Structural Upscale

Interpretasi Geologi

Model Horison

Trend Modeling

Distribusi Phi,K,Sw,NtG mengacu terhadap Facies / Rocktype

Analisis Ketidakpastian

Properties Upscale

Analisis Teknik Reservoir

Zonasi

Distribusi Facies Validasi dengan Data Dynamic

Pembuatan Lapisan

Integrasi Konsep Geologi

Grid Quality Control

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KEBUTUHAN DATA

KEBUTUHAN DATA Intepretasi Geofisika

Intepretasi Geologi

Intepretasi Petrofisika

Korelasi Sumur

Porositas

Bubble Map

Saturasi Air

Analisis Uji Sumur

Interpretasi Seismik Atribut Seismik

Analisis Teknik Reservoir

Boi & Bg

Fasies Geologi

Konseptual Sebaran Fasies (Peta 2D)

Permeabilitas Rock Type Kontak Fluida Persamaan Saturasi Diatas Kontak

* Tipikal data pada reservoir konvensional, dapat berbeda pada kasus reservoir unconventional

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MODEL GRID •

Objektif

•

Workflow

•

Model Patahan

•

Areal Gridding

•

Model Horison dan Zone

•

Model Lapisan

•

Scale up Well Log

•

Grid Quality Control

•

Studi Kasus 1 (Lapangan Bravo)

•

Studi Kasus 2 (Lapangan KE)

OBJEKTIF • Membangun arsitektur dari reservoir dengan membaginya menjadi grid block dengan ukuran yang konsisten terhadap resolusi data statik • Menggabungkan patahan dan horison hasil interpretasi seismik • Membagi zona berdasarkan kombinasi data seismik dan sumur • Membagi perlapisan pada tiap zona berdasarkan kondisi geologi

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WORKFLOW

Model Patahan

Areal Gridding

Model Horison

Quality Control

Model Zona

Model Lapisan

WORKFLOW

Bahar, 2012

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MODEL PATAHAN

MODEL PATAHAN TUJUAN: Memasukkan hasil Patahan interpretasi seimik kedalam Model Grid HAL YANG HARUS DIPERHATIKAN: • Patahan yang dimodelkan sebaiknya HANYA patahan yang berkontribusi terhadap geometri dan properti reservoir • Geometri Patahan: Vertikal, Miring, Listrik • Hubungan antar patahan (Memotong secara lateral/Vertikal*) • Smoothing dan editing sebaiknya melihat kembali data seismik (lakukan terlebih dahulu pada domain time) karena akan mempengaruhi volume reservoir • Kaidah geologi struktur * Patahan yang memotong secara vertikal akan mempengaruhi bentuk grid, biasanya memerlukan perhatian khusus. Lebih baik dihindari

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MODEL PATAHAN HAL YANG HARUS DIPERHATIKAN: • Patahan yang dimodelkan sebaiknya HANYA patahan yang berkontribusi terhadap geometri dan properti reservoir

Dimodelkan atau tidak?

Man in Charge: Geologist dan Reservoir Engineer

MODEL PATAHAN

Fault memotong secara lateral

Fault memotong secara vertikal

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MODEL PATAHAN Common Practice: -

Kumpulkan semua patahan hasil interpretasi, diskusikan bersama geologist dan reservoir enggineer patahan mana saja yang akan dimodelkan.

-

Tentukan bentuk dari masing masing patahan. Untuk model skala reservoir biasanya pilar linear dengan 2 atau 3 poin sudah cukup untuk memodelkan patahan.

-

Pastikan apakah terdapat patahan yang berpotongan secara vertikal, jika ada diskusikan kembali dengan geologi dan geofisika apakah kedua patahan tersebut penting, jika ia maka diperlukan perhatian khusus.

-

Transfer patahan hasil interpretasi ke dalam model grid.

-

Lakukan editing dan smoothing dengan melihat kembali data Seismik.

-

Diskusikan apakah hasil model patahan sudah baik dari sisi geologi, geofisika dan reservoir.

AREAL GRIDDING

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AREAL GRIDDING TUJUAN: Membuat grid secara lateral yang meggambarkan heterogenitas secara areal. HAL YANG HARUS DIPERHATIKAN: •

Usahakan berbentuk rectangular (segi empat)

•

Ukuran minimum: Resolusi seismik

•

Ukuran maksimum: Sediakan minimum 2 atau 3 grid blok diatara sumur

•

Usahakan tidak ada 2 atau lebih sumur dalam satu grid, kecuali twin well atau beroperasi pada waktu yang berbeda

•

Jangan berencana untuk melakukan areal upscale

AREAL GRIDDING

Contoh 1: Patahan tidak diberi arah mengakibatkan banyak grid tidak berbentuk segi empat

Contoh 2: Patahan diberi arah, grid berbentuk segi empat

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AREAL GRIDDING

Contoh 3: Patahan kompleks tanpa diberi arah

Contoh 4: Patahan kompleks setelah diberi arah

AREAL GRIDDING

Ukuran grid =200 * 200 Total Grid = 1,964,025 2 sumur pada 1 grid

Ukuran grid =100 * 100 Total Grid = 3,928,050

Ukuran grid =50 * 50 Total Grid = 15,712,200 Total grid terlalu besar

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AREAL GRID Common Practice: - Tentukan area yang ingin dimodelkan. - Buat batasan model berupa poligon, usahakan searah dengan patahan utama. - Berikan arah pada setiap patahan yang berarah sama, manfaatkan fitur “Automatic direction assignment” pada perangkat lunak pemodelan - Tentukan besaran grid yang paling sesuai pada model yang akan dibangun - Periksa hasil grid, apakah terdapat grid yang masih bisa dioptimasi

MODEL HORISON DAN ZONE

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MODEL HORIZONE TUJUAN: Integrasi hasil korelasi sumur dan intepretasi seismik (fault dan horison) kedalam model pilar yang telah dibuat. HAL YANG HARUS DIPERHATIKAN: •

Horison yang dimodelkan sebaiknya berasal dari hasil intepretasi seismik

•

Residual marker dan horison telah diminimalisir agar hasil model tidak terdapat bull eyes

•

Jarak pengaruh dari masing masing patahan

•

Jarak displacement maksimum dan minimum patahan

MODEL HORISON

Input horison

Hasil model

Jarak pengaruh patahan

Input data yang terkena pengaruh patahan akan dihilangkan, kemudian interpolasi dari data yang berada diluar pengaruh patahan

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MODEL ZONE

TUJUAN: Membagi lapisan didalam horison yang tidak dapat didapatkan melalui intepretasi seismik. HAL YANG HARUS DIPERHATIKAN: • Zonasi dibagi berdasarkan konsep geologi (Chrono / Lito)

MODEL LAPISAN

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MODEL LAPISAN PHI

SW

NTG

TUJUAN: Membagi setiap lapisan reservoir menjadi lapisan tipis sesuai dengan resolusi data (fine layer) HAL YANG HARUS DIPERHATIKAN: • Ukuran lapisan harus dapat mencapture tingkat heterogenitas vertikal reservoir • Tipe Layering • Jumlah total grid cell

MODEL LAPISAN

Yerus dan Chambers, 2006

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SCALE UP WELL LOG

SCALE UP WELL LOG

TUJUAN: Memasukkan nilai sumuran kedalam grid block HAL YANG HARUS DIPERHATIKAN: •

Data log sumur

Metode scale up

Hasil Upscale

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GRID QUALITY CONTROL

GRID QUALITY CONTROL

Evaluasi histogram data log sumur dan hasil scale up. Jika perbedaan cukup signifikan, perbanyak jumlah layer pada zona yang bermasalah

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GRID QUALITY CONTROL Periksa nilai volume dari tiap grid. Nilai minus menunjukkan bahwa ada grid yang terlipat, periksa tahapan areal grid.

FACIES MODELING

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TOPICS •

What is Facies, Rock Type, and Facies Modeling ?

• Why do we need to do Facies Modeling ? • How do we do Facies Modeling ? • “Facies” at Well Location • 3D “Facies” Distribution • Case Study Example of Facies Modeling.

GEOLOGICAL FACIES Definition : •

Facies are a body of rock with specified characteristics.

•

Ideally, a facies is a distinctive rock unit that forms under certain conditions of sedimentation, reflecting a particular process or environment

•

Facies are distinguished by what type of the rock is being studied (e.g., Lithofacies (based on petrological) , Biofacies (based on fossil),

•

Lithofacies classifications are a purely geological grouping of reservoir rocks, which have similar texture, grain size, sorting etc.

•

Each lithofacies indicates a certain depositional environment with a distribution trend and dimension.

•

Knowledge in Facies is important as it provides information on how the rock is ditributed in the reservoir

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RESERVOIR ROCK TYPE Definition : • RRT is grouping of geological rock based on both geological facies and petrophysical grouping (porosity, permeability, capillary pressure and saturation). • The objective of generating RRT is to link property with geology • Facies distribution may be interpreted by geological knowledge but not necessarily the property due to diagenesis

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FACIES MODELING TECHNIQUES

FACIES MODELING Gaussian Simulation

TGS

SIS

Well log

Trend Property

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ROCK TYPE MODELING Well log

Gaussian Simulation

TGS

Constraint to Facies model

Facies Modelling

Reflection strength attribute

Facies model

Rocktype Model

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KEY ISSUE IN FACIES MODELING • Conceptual Geological Model is needed in order to QC the result and/or used as the trend. • Integration with other information, other than well data, in the form of 2D or 3D distribution is critical in order to obtain reliable result. • Possible trend for Facies Modeling : • Seismic Data • Probability Map of Facies Distribution • Diagenesis Model

PETROPHYSICAL MODELING

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WHY DO WE DO PETROPHYSICAL MODELING? • To obtain 3D distribution of porosity consistent with it’s geological (facies) distribution. • It is one of the most important component for quantifying the volumetric of the reservoir. Primary Data : • Attribute at Well Locations, obtained from : • Petrophysical Analysis / Well Log Interpretation (PHIE). The analysis should consider core-log correlation. Secondary Data : • 3D Facies Model • 2D or 3D Seismic Attributes (e.g., AI, Amplitude) Spatial Information • Calculated from well data (at least vertical variogram), if sufficient well data exists, or • Inferred from Seismic Attributes (Correlation Length and direction)

• Constraint To Rocktype • Linear relationships / Simulation

Water Saturation

• Constraint To Rocktype • Guided by Seismic Attribute • SIS

Permeability

• Constraint To Rocktype • Guided by Seismic Attribute • SIS

Porosity

Vsh

PROPERTIES MODEL

• Constrain to Rocktype • Saturation height function i.e. J-Function

Key Issues: Good 3D Facies Model and/or good correlation with Seismic Attribute (e.g., Acoustic Impedance) is essential for the success of Porosity Modeling

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VOLUMETRIC CALCULATION

VOULUMETRIC CALCULATION

Each cells have its own values

STOIIP = Bv * NtG * Porosity * (1-Sw) *(1/Boi)

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UNCERTAINTY IN THE MODELING

“Its is better to have uncertainty rather than illusion of reality” Andre G. Journel

More is the hard data we have , less is the uncertainty in the model Calculating the uncertainty in the model, tells us how realistic is the Model made with the available data

Uncertainty in the Modeling What adds to uncertainty in the model • Errors/uncertainty in seismic interpretation • Errors/Uncertainty in Velocity Modeling if time to depth conversion was involved • Errors/uncertainty in the log data processing • Errors/uncertainty in data analysis • Errors/Uncertainty in 3D interpolation Uncertainty in the Model is a Cumulative Result of all the above mentioned factors

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SENSITIVITY AND UNCERTAINTY

SENSITIVITY AND UNCERTAINTY

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SENSITIVITY AND UNCERTAINTY Contact

Variogram

Permeability Sw Cutoff Boi

SENSITIVITY AND UNCERTAINTY

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SENSITIVITY AND UNCERTAINTY

End of Slide Show

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History Start in 2012 this research group dedicated to educate young researcher to develop the country especially in energy resources.

What we do Study of oil and gas area related to Formation Evaluation research field, Join Discussion Group, Training, Seminar, and Project.

Experience              

SOP Petrophysical Multimin Dual Water Saturation Shally Sand and Dual Porosity Carbonate. UTC Pertamina. October 2012 – April 2013. G&G Study MAC and MDK Field. Husky-Cnooc Madura Ltd. April – June 2013. Petrophysical analysis of MMC Parigi. ETTI – Pertamina EP. July – Augustus 2013. G&G Basic Training. Pusat Survey Geologi. Augustus – September 2013. G&G Study of Kenali Asam Dangkal Field. EOR Pertamina. October – December 2013. Provision of Basin Study and Petroleum System of West Galagah kambuna Block, North Sumatra Basin. Petronas Carigali (West Galagah kambuna) Ltd. December 2013 – May 2014. GGRPFE Study of South jambi B Field. Pertamina Hulu Energy. Maret – Oktober 2014. SOP Rock Typing and Static Model Carbonate and Silisiclastic. UTC Pertamina. January – October 2014. Studi Karakterisasi Reservoir Gas Metana Batubara (CBM) Cekungan Sumatra Selatan, Barito, dan Kutai. Pertamina Hulu Energy. On Going. G& G Betun Selo Field . PT Petroenim Betun Selo, February 2012 Petrophysical Training , PT. Tropic Energy, 2013 Resertifikasi Cadangan Struktur Donggi, matindok, Maleoraja, dan Minahaki, Sulawesi tengah, MGDP Pertamina EP GGR Study of Badik Structure , PHE Nunukan, on going